In a recent New York Times piece throwing skeptical shade at the promise of Artificial General Intelligence, Cade Metz notes splashy predictions from Sam Altman, Dario Amodei and Elon Musk, all forecasting AGI within a handful of years—or even months.
Let's back up for a sec, though...what do we even mean by "AGI"? AGI would essentially mark the debut of the first machine to learn, reason and adapt across essentially any cognitive task with human‑level versatility (or beyond). Current generative models, impressive as they are, solve tightly defined problems.
By now, we’re used to headlines that trumpet breakthroughs: GPT‑4 passes legal exams! DeepMind’s AlphaCode competes with seasoned software engineers! reinforcement‑learning agents master Go!
But beneath the buzz, experts seem to want everyone to calm down a bit when it comes to AGI.
“What we are building now are things that take in words and predict the next most likely word….That’s very different from what you and I do,” Frosst told the NYT. Harvard’s Pinker added that these systems are “very impressive gadgets,” not miracle minds.
Stagwell Marketing Cloud’s Head of AI Solution Development, Louis Criso, has previously echoed such sentiments: “If an LLM is acting ‘like a human,’ people can understandably start to feel that it’s real—it’s perceiving, it’s thinking. But all the GenAI is doing here is literally just mimicking. That’s it. The results can be impressive, but it’s not magic.”
Metz walks NYT readers through an AGI consensus among researchers that’s less rosy than that of buzzy tech founders: Today’s large neural networks are powerful, but they remain pattern‑matching machines that lack common‑sense reasoning, physical grounding and an understanding of causality. At least, so far.
A survey of the Association for the Advancement of Artificial Intelligence (AAAI) reinforces the skepticism—more than three‑quarters of respondents see fundamental limits in today’s techniques—that "scaling up current AI approaches" to yield AGI is "unlikely" or "very unlikely" to succeed.
The overarching takeaway: AGI remains an aspirational target, not an imminent reality.
Imagine, for a moment, that an authentic AGI arrives. What changes? Are you out of a job, or cowering in fear before your new robot boss?
In many ways, a martech world in which AGI is a reality would be a dramatic escalation of the tools we currently have.
Here’s what that might look like:
These scenarios are compelling, but—as Metz’s reporting underscores—they remains hypothetical. That said, it’s all the more reason for marketing professionals to master current GenAI tools, rather than assuming this technology will fade away as a fad or trend.
Researchers point to missing ingredients of true AGI—grounded perception, causal reasoning, reliable alignment—that are unlikely to materialize just by adding GPUs. For now, the smart move for marketers is two‑fold:
So yes, AGI may radically disrupt and transform marketing one day, but the expert consensus remains that this day is not just around the corner.
The GenAI tools within every marketer’s current reach are impressive in their own right—they’re just not sentient algorithms that can run a brand or agency with the push of a button.
